High stone-free rate immediately after suctioning flexible ureteroscopy with Intelligent pressure-control in treating upper urinary tract calculi.

BACKGROUND: The retrospective observational study aimed to evaluate the safety and efficacy of suctioning flexible ureteroscopy with Intelligent pressure-control (SFUI) on treating upper urinary tract calculi in a large cohort. METHODS: Between July 2020 and August 2021, 278 patients with upper urinary tract calculi who underwent SFUI in our hospital were enrolled. Outcomes were stone-free rate (SFR) in one session and one-month after SFUI treatment, and complications scored by the Clavien-Dindo classification. RESULTS: A total of 310 kidneys underwent SFUI were included. The median surgery time was 75 min (ranged 60-110 min). One session and one-month SFRs were 80.65% and 82.26%, respectively. The one-session SFR was ≧ 87% in patients with Guy's stone score of Grade I among stone size < 40 mm. Risk factors for unsuccessful stone-free in one session were stone history (adjusted odds ratio (aOR): 2.39, 95% confidence interval (CI): 1.21-4.73), stone size of 40-49 mm (aOR: 4.37, 95% CI: 1.16-16.45), Guy's stone score ≧ Grade II (Grade II, aOR: 3.54, 95% CI: 1.18-10.59; Grade III, aOR: 10.95, 95% CI: 2.65-45.25). The incidence of Clavien-Dindo grade II-III complication was 3.26%. Complication is associated with Guy's stone score III (aOR: 22.36, 95% CI: 1.81-276.36). CONCLUSION: SFUI shows good safety and efficiency on treating upper urinary tract calculi. Patients with stone size < 40 mm or Guy's stone score of Grade I have a high chance to reach stone-free after SFUI treatment.

An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data.

Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the "true" interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.

Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. Although the correlation-based gene networks are computationally affordable, they have limitations after being applied to gene expression time-course data. We proposed Temporal Gene Coexpression Network Analysis (TGCnA) framework for the transcriptomic time-course data. The mathematical nature of TGCnA is the joint modeling of multiple covariance matrices across time points using a 'low-rank plus sparse' framework, in which the network similarity across time points is explicitly modeled in the low-rank component. We demonstrated the advantage of TGCnA in covariance matrix estimation and gene module discovery using both simulation data and real transcriptomic data. The code is available at https://github.com/QiZhangStat/TGCnA.